surveillance: twinstim_profile – R documentation

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twinstim_profile

Profile Likelihood Computation and Confidence Intervals for twinstim objects

Description

Function to compute estimated and profile likelihood based confidence intervals for twinstim objects. Computations might be cumbersome!

WARNING: the implementation is not well tested, simply uses optim (ignoring optimizer settings from the original fit), and does not return the complete set of coefficients at each grid point.

Usage

## S3 method for class 'twinstim'
profile(fitted, profile, alpha = 0.05,
        control = list(fnscale = -1, maxit = 100, trace = 1),
        do.ltildeprofile=FALSE, ...)

Arguments

`fitted`	an object of class `"twinstim"`.
`profile`	a list with elements being numeric vectors of length 4. These vectors must have the form `c(index, lower, upper, gridsize)`. `index`: index of the parameter to be profiled in the vector `coef(fitted)`. `lower, upper`: lower/upper limit of the grid on which the profile log-likelihood is evaluated. Can also be `NA` in which case `lower/upper` equals the lower/upper bound of the respective 0.3 % Wald confidence interval (+-3*se). `gridsize`: grid size of the equally spaced grid between lower and upper. Can also be 0 in which case the profile log-likelihood for this parameter is not evaluated on a grid.
`alpha`	(1-alpha)% profile likelihood based confidence intervals are computed. If alpha <= 0, then no confidence intervals are computed. This is currently not implemented.
`control`	control object to use in `optim` for the profile log-likelihood computations. It might be necessary to control `maxit` or `reltol` in order to obtain results in finite time.
`do.ltildeprofile`	If `TRUE` calculate profile likelihood as well. This might take a while, since an optimisation for all other parameters has to be performed. Useful for likelihood based confidence intervals. Default: `FALSE`.
`...`	unused (argument of the generic).

Value

list with profile log-likelihood evaluations on the grid, and – not implemented yet – highest likelihood and Wald confidence intervals. The argument profile is also returned.

Author(s)

Michael Höhle

Examples

# profiling takes a while
## Not run: 
#Load the twinstim model fitted to the IMD data
data("imdepi", "imdepifit")
# for profiling we need the model environment
imdepifit <- update(imdepifit, model=TRUE)

#Generate profiling object for a list of parameters for the new model
names <- c("h.(Intercept)","e.typeC")
coefList <- lapply(names, function(name) {
  c(pmatch(name,names(coef(imdepifit))),NA,NA,11)
})

#Profile object (necessary to specify a more loose convergence
#criterion). Speed things up by using do.ltildeprofile=FALSE (the default)
prof <- profile(imdepifit, coefList,
  control=list(reltol=0.1, REPORT=1), do.ltildeprofile=TRUE)

#Plot result for one variable
par(mfrow=c(1,2))
for (name in names) {
  with(as.data.frame(prof$lp[[name]]),
       matplot(grid,cbind(profile,estimated,wald),
               type="l",xlab=name,ylab="loglik"))
  legend(x="bottomleft",c("profile","estimated","wald"),lty=1:3,col=1:3)
}

## End(Not run)

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30